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Analytics builder: Keep track of vital learning insights

Analytics essentially refers to various tools and processes used to analyze data.  

Data analytics plays a pivotal role in various industries; it helps them learn customer trends, streamline operations, and predict future outcomes for organizations. Investing in analytics has been a great help for all sorts of companies.

 According to Market Research Future (MRFR), the global learning analytics market is set to grow by 26% CAGR by 2023, propelled by the explosion of smartphone usage across the globe. (Source: Market Press Release)

 Be it a traditional business like BFSI, where it is used to analyze the value stocks in the future or the healthcare industry where data points collected from patients are used to understand patient health and help patients from future diseases. 

Analytics is used across non-traditional businesses like entertainment where they collect user’s data points and provide recommendations to their liking, similar to Spotify and Netflix. In general, analytics improves user experience, and the learning and development revolution has just begun.  

 L&D Analytics

 Under L&D departments, we can collect data points of employees and use them to identify better learning strategies. It is available for users, managers, and the L&D team, and each one can develop a better learning plan that has the best impact on employee job performance.

 Data provided in enterprise software like LMS and LXP is analyzed and represented through its analytics builder. Below are the various insights gathered from analytics builder and used to create a better experience for its users and administrators.

1.      Benchmarking skills

           Analytics builder also provides detailed insight on current existing skills and skill gaps. Learning software like Disprz uses AI-enabled Skill architect where soft, functional, technical skills required for various roles are picked across multiple platforms and are benchmarked for each position. Based on the benchmark, employees are recommended a better learning path to reach the goal.

           2.                  Convert Raw Data into actionable insights 

 Data points aren’t of much use until they are represented through graphs and charts. Learning software can develop them and provide various measurements in their dashboard, accessible for users and administrators. These insights are simple to understand and very simple to set up.

3.                  Track your learner’s login frequency and usage pattern 

 Administrators or managers can also develop reports on how active the employee is. Based on the login frequency, they can understand the issue faced by learners and help to create a dynamic learning platform.

4.                  Identify hotspots of low learning/engagement in your organization. 

  Learning software can help administrators or managers overview the training courses currently running and their participation rate. If users do not pick up any training, they can re-develop and make it more engaging and exciting.

 5.                  With in-depth learning, analysis determines what factors led to a particular outcome.

 Learning software can evaluate the outcome of each user in each program and compare it with job metrics. For example, suppose the company has rolled out a new ERP software for running tasks. In that case, it can implement a training program in learning software and compare the active users in ERP software and understand if the training was successful or not. 

6.                  Data management 

 AI-powered software is a boon for administrators and users since dashboards can track progress and limit time spent preparing impacts and trends on spreadsheets. The customized dashboards provide highly visualized reports that users can share quickly across the team. 

7.                  Improves employee engagement 

 The best feature of Analytics builder is its content recommendation tool, where users are provided suggested content based on their skill gap, roles, responsibilities, and likes or dislikes on the content format. It also provides insights on learner’s growth and how long it might take to complete the program and a personalized report to keep track of their progress. Such statements have helped the learners be more engaged in learning. 

8.                  More research options

 AI predictive analysis can be used to develop future insights on learner skill growth or time taken to complete programs or to understand if managers can use employee skills across other departments or better roles in the future. These future insights are a great way to know how agile the workforce can be if the company continues the existing learning program. 

 Conclusion

 The use of analytics to incorporate learning will foster high learning program standards and help companies upskill and motivate their employees. 

  As we continue to look to the future, it is predicted that learning analytics with learning software will bring together better storage, management, and data analysis, which will help companies develop a robust learning environment.